Bayesian Analysis of a Markov Switching Stochastic Volatility Model
نویسندگان
چکیده
This article analyzes a Markov switching stochastic volatility (MSSV) model to accommodate the shift in the mean of log-volatility. Since it is difficult to estimate the parameters in this model based on the maximum likelihood method, a Bayesian Markov-chain Monte Carlo (MCMC) approach is adopted. A particle filter for the MSSV model, which is used for model comparison and diagnostics, is constructed. The estimation result, based on weekly returns of the TOPIX, confirms the finding by previous researchers that the estimate of the persistence parameter drops and the estimate of the error variance rises in the volatility equation of the MSSV model compared to those of the standard SV model. The model comparison provides evidence that the MSSV model is favored over the standard SV model. It is also found that the MSSV model passes the diagnostic tests based on the statistics obtained from the particle filter while the SV model does not.
منابع مشابه
Markov Regime Switching Stochastic Volatility
This is a project on modeling time-varying volatility of S&P 500 weely return for the years 1990 to 2012 using Bayesian methods. First, MCMC on the log-stochastic volatility (SV) model is implemented with simulation results analyzed. Second, I generalize the SV model to encompass regime-switching properties with the markov switching log-stochastic volatility (MSSV) model, under which, high-vola...
متن کاملBayesian Inference for Markov Switching Stochastic Volatility Models
We study a Markov switching stochastic volatility model with heavy tail innovations in the observable process. Due to the economic interpretation of the hidden volatility regimes, these models have many financial applications like asset allocation, option pricing and risk management. The Markov switching process is able to capture clustering effects and jumps in volatility. Heavy tail innovatio...
متن کاملBayesian inference for regime switching stochastic volatility model with fat-tails and correlated errors
The stochastic volatility model provides a successful tool in accounting for well-documented time-varying behavior in the volatility of financial time series. The purpose of the paper is to extend the regime-switching stochastic volatility (RSSV) model to handle with both heavy-tailed and correlated errors. A Bayesian approach with Markov Chain Monte Carlo is employed for estimating model param...
متن کاملMarkov-Switching and Stochastic Volatility Diffusion Models of Short-Term Interest Rates
This paper empirically compares the Markov-switching and stochastic volatility diffusion models of the short rate. The evidence supports the Markov-switching diffusion model. Estimates of the elasticity of volatility parameter for single regime models unanimously indicate an explosive volatility process, while the Markov-switching models estimates are reasonable. We find that either Markov-swit...
متن کاملHas Tehran Stock Market Calmed Down after Global Financial Crisis?Markov Switching GARCH Approach
We have introduced an early warning system for volatility regimes regarding Tehran Stock Exchange using Markov Switching GARCH approach. We have examined whether Tehran Stock Market has calmed down or more specifically, whether the surge in volatility during 2007-2010 global financial crises still affects stock return volatility in Iran. Doing so, we have used a regime switching GARCH model. ...
متن کامل